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Use OpenClaw onboarding wizard for Plano provider setup
Replace manual JSON config with instructions to use the openclaw onboard wizard to set up a custom OpenAI-compatible provider pointing at Plano. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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2 changed files with 30 additions and 32 deletions
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@ -44,38 +44,39 @@ planoai up --service plano --foreground
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### 3. Set Up OpenClaw
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If you haven't installed OpenClaw yet:
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Install OpenClaw (requires Node >= 22):
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```bash
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npm install -g openclaw@latest
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```
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Install the gateway daemon and connect your messaging channels:
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```bash
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npm install -g openclaw@latest # requires Node >= 22
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openclaw onboard --install-daemon
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```
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The onboarding wizard will walk you through connecting your messaging channels (WhatsApp, Telegram, Slack, Discord, etc.) and install the gateway as a background service.
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This installs the gateway as a background service (launchd on macOS, systemd on Linux). To connect messaging channels like WhatsApp or Telegram, see the [OpenClaw channel setup docs](https://docs.openclaw.ai/gateway/configuration).
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Run `openclaw doctor` to verify everything is working.
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### 4. Point OpenClaw at Plano
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Edit `~/.openclaw/openclaw.json` to route all LLM requests through Plano:
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During the OpenClaw onboarding wizard, when prompted to choose an LLM provider:
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```json
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{
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"agent": {
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"model": "kimi-k2.5",
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"baseURL": "http://127.0.0.1:12000/v1"
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}
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}
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```
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1. Select **Custom OpenAI-compatible** as the provider
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2. Set the base URL to `http://127.0.0.1:12000/v1`
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3. Enter any value for the API key (e.g. `none`) — Plano handles auth to the actual providers
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4. Set the context window to at least `128000` tokens
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Then restart the gateway to pick up the change:
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This registers Plano as OpenClaw's LLM backend. All requests go through Plano on port 12000, which routes them to Kimi K2.5 or Claude based on the prompt content.
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If you've already onboarded, re-run the wizard to update the provider:
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```bash
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openclaw onboard --install-daemon
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```
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That's it — OpenClaw now sends all LLM requests to Plano on port 12000, and Plano routes them to the best model based on the prompt content.
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### 5. Test Routing Through OpenClaw
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Send messages through any connected channel (WhatsApp, Telegram, Slack, etc.) and watch routing decisions in a separate terminal:
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@ -1,11 +1,17 @@
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version: v0.3.0
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version: v0.1.0
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routing:
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model: Arch-Router
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llm_provider: arch-router
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listeners:
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- type: model
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name: model_listener
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egress_traffic:
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address: 0.0.0.0
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port: 12000
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message_format: openai
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timeout: 30s
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model_providers:
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llm_providers:
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# Kimi K2.5 — Moonshot AI's open model (1T MoE, 32B active params)
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# Great for general conversation, agentic tasks, and multimodal work
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@ -15,22 +21,13 @@ model_providers:
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base_url: https://api.moonshot.ai/v1
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default: true
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routing_preferences:
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- name: general conversation
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description: general chat, greetings, casual conversation, Q&A, and everyday questions
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- name: agentic tasks
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description: coordinating multi-step workflows, device automation, scheduling, and task orchestration across channels
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- name: code generation
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description: generating code, writing scripts, implementing functions, and building tool integrations
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# Claude — Anthropic's most capable model
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# Best for complex reasoning, code, tool use, and evaluation
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- model: anthropic/claude-sonnet-4-5
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access_key: $ANTHROPIC_API_KEY
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routing_preferences:
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- name: testing and evaluation
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description: writing tests, running evaluations, QA checks, verifying correctness, and debugging failures
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- name: code generation
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description: generating code, writing scripts, implementing functions, and building tool integrations
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- name: complex reasoning
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description: multi-step analysis, planning, architectural decisions, and deep problem-solving
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tracing:
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random_sampling: 100
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- name: general conversation
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description: general chat, greetings, casual conversation, Q&A, and everyday questions
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